Combining the strengths of UMIST and The Victoria University of Manchester An analysis of the relationship between time spent on active leisure and educational qualifications Shu-li Cheng Centre for Research on Innovation and Competition (CRIC) University of Manchester Presentation to UPTAP Workshop, Leeds, 21 st March 2007

Combining the strengths of UMIST and The Victoria University of Manchester Objectives Applying zero-inflated modelling approach to estimate the amount of time spent on infrequent activities in the UK 2000 Time Use Survey –The typical distribution of time spent on an infrequent activity during survey period is highly positively skewed and has a large proportion of zeros To examine the relationship between educational qualifications and active leisure allowing for potential socio-economic and demographic characteristics using the survey –The effect of education on participation in active leisure –The effect of education on the amount of time spent on active leisure

Combining the strengths of UMIST and The Victoria University of Manchester The UK 2000 Time Use Survey The main aim of the survey is to measure the amount of time spent by the UK population on various activities. Stratified random sampling was used to collect the data –People aged 8 years and over from the randomly selected household were interviewed and were asked to record their activities in the given diaries »Note: only respondents aged 16 and over are included in the following analysis Two diaries per respondent –One weekday and one weekend day The diary was pre-divided into 10-minute intervals –Primary and secondary activities were recorded –Information on with whom the primary activity was carried out

Combining the strengths of UMIST and The Victoria University of Manchester Zero-inflated modelling (mixture model)

Combining the strengths of UMIST and The Victoria University of Manchester Response variables (1) Walking Walks, rambles Other outdoor hobbies (e.g. painting) Include general “outdoors” variables Actively participating in sports Outdoor team games Non-team ball hitting sports Running, jogging, cross-country, track and field Golf Fishing Bowls Martial arts Swimming and other water sports Keep fit, yoga, aerobics, dance practice Cycling Other outdoor sports Other indoor sports Horse rides Hunting, shooting, fishing, etc. Other participation in sport and active leisure activities Include communication for the purposes of active leisure Include general variables such as “other” active leisure or “other” sport

Combining the strengths of UMIST and The Victoria University of Manchester Response variables (2)

Combining the strengths of UMIST and The Victoria University of Manchester Explanatory variables Educational qualifications –Incomplete secondary or less (reference category) –Complete secondary –Above secondary Sex –Man (reference category) –woman Age Marital status –Married/cohabiting (reference category) –Not living with a spouse or partner Number of children under age 18 in the household Hours of paid work last week Income –Lowest 25% –Middle 25% (reference category) –Highest 25% Whether respondent has disability –No (reference category) –Yes Urban or rural household –Urban/suburban (reference category) –Rural/semi-rural Weekday or weekend –Weekday (reference category) –Weekend

Combining the strengths of UMIST and The Victoria University of Manchester Results: time spent walking

Combining the strengths of UMIST and The Victoria University of Manchester Whether spent time on walking (logit model) All explanatory variables are statistically significant. Educational qualifications are important in predicting the likelihood of spending time on walking. –Those gaining qualifications above secondary level are significantly more likely to spend time walking compared with those gaining no qualifications. But there is no significant difference between secondary level and below secondary. Men are more likely to walk than women as well as not married than married. The least income group is more likely to spend time walking than the middle income group. But no significant difference between middle and highest groups. The likelihood of spending time walking decreases with age, number of dependent children, hours of work, as well as having disability and being in a rural household. The likelihood of walking is significantly higher at the weekend than on a weekday.

Combining the strengths of UMIST and The Victoria University of Manchester Time spent walking (negative binomial model) The amount of time spent walking is 1.1 times longer for a man, compared with time spent by a woman. The amount of time the lowest income group spent on walking is 1.1 times longer than the middle income group. Time spent walking at the weekend is 1.2 times longer than on a weekday.

Combining the strengths of UMIST and The Victoria University of Manchester Results: time spent actively participating in sports

Combining the strengths of UMIST and The Victoria University of Manchester There is a gradient among educational groups on the likelihood of spending time on active sports. –The most educated are the most likely group to spend time on active sports, while the least educated are the least likely to participate. Men are more likely to participate than women. The likelihood of participating decreases with age as well as hours doing paid work. Not married people are more likely to spend time on active sports than those married. Similar to the impact of education, the effect of income shows an upward gradient on the likelihood of participation. Disability has a negative impact on participating in active sports. Whether spent time on active sports (logit model)

Combining the strengths of UMIST and The Victoria University of Manchester Time spent on active sports (negative binomial model) Education is not statistically significant in predicting time spent on active sports after controlling for other explanatory variables. Gender, hours of paid work, income and weekend are the significant variables. –The time a man spent on active sports is 1.5 times longer than a woman. –An additional hour of paid work reduces about 0.3% of time on active sports. –The amount of time spent by the lower income group is 84% of the middle income group. –The amount of time spent on active sports at the weekend is about 1.3 times longer than on a weekday.

Combining the strengths of UMIST and The Victoria University of Manchester Conclusion The benefits of zero-inflated modelling approach –Allowing for structural zeros and sampling zeros in the data makes the model more general –The influence of explanatory variables can be examined in both logit part and negative binomial (Poisson) part of the model The effect of educational qualifications is significant for participating in leisure walking and in active sports after controlling for other explanatory variables; however, no difference among educational levels on the amount of time spent. Gender differences are found in active leisure. Men are more likely to participate than women. They also spend longer time doing it. Income influences both participation and the amount of time spent on active leisure. The effect seems more obvious for the lower income group. Day of the week is highly significant in predicting time spent on active leisure. –More time spent over the weekend on active leisure than during the week after taking into account of hours of paid work and other explanatory variables.